This paper presents an in-depth analysis of state-of-the-art semantic segmentation algorithms applied to spacecraft safe planetary landing via hazard detection and avoidance. Several architectures are trained from binary safety maps and the rich dataset of the High-Resolution Imaging Science Experiment (HiRISE) embedded on Mars Reconnaissance Orbiter for realistic purposes. The study incorporates several metrics comparisons such as recognition accuracy, computational complexity, model complexity, and inference time. The proposed performance indices and combinations are analyzed and discussed. The experiments were performed using a Raspberry Pi 4B, which is a relevant commercial-of-the-shelf microcontroller surrogate of NASA's High-Performance Spaceflight Computer (HPSC) that will thrive within the next decades in space exploration. This paper allows researchers to know what has been tested on the subject and serves as a catalog for users to pick the most relevant architecture for their own application.

This paper introduces ∆-MILP, a powerful variant of the mixed-integer linear programming (MILP) optimization framework to solve NASA's Deep Space Network (DSN) scheduling problem. This work is an extension of our original MILP framework

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